CHAPTER 5: RESEARCH FINDINGS
5.2 Data Analysis Types and Tools
Most research papers have two different categories of data analysis procedures, qualitative data analysis and quantitative data analysis (Shields, 2017). There are drawbacks and advantages related to each method. The data which do not have any sort of numerical value but which are full of theories and models are evaluated on the basis of qualitative analysis and the data which have numerical value are evaluated on the basis of the quantitative technique (Awang et al., 2016). Quantitative data are considered by the researcher in this study.
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Analysis of research papers deploys diverse categories of tools to analyse the data which are gathered from primary foundations such as R programming, which is a type of analytic tool, tableau, which is much useful to create a data visualization, dashboards and data maps (Disman, Ali and Education, 2017). Programming languages like Python are also very significant to analyse diverse categories of raw data sets. Data manipulation can be performed with the help of another programming language such as SaS, which was developed in 1966. Large-scale processing of data can also be performed using a data processing engine known as Apache Spark (Lajoie, 2016). Microsoft Excel is also very useful to summarise any type of raw data with the help of pivot tables. Rapid Miner is one of the most popular data science platforms and is very useful to analyse any type of real-time data. The predictive analytics capabilities of Rapid Miner are increasing due to the incorporation of machine-learning algorithms (Bloomberg and Volpe, 2018). Knime is also considered as one of the biggest data tools which is very useful to interpret any data using visual programming (Lewis, 2016). Splunk is a statistical tool which is very useful to analyse any type of text-based data (Disman, Ali and Barliana, 2017). The other essential data analysis tool which is often used by academic students is Statistical Package for Social Sciences (SPSS); this tool is one of IBM’s most popular products and is very useful to analyse any type of data organised as a database.
The data analysis tool which has been selected in this thesis is SPSS; this tool imports data from other data sources and then analyses those data with the help of graphical illustrations and figures (Friese, 2019). There are drawbacks as well as benefits associated with the deployment of this software. Research analysts can add variables as per the paper’s hypothesis using this tool. This tool is also much useful to assign different properties to the research variables (Disman, Ali and Barliana, 2017). This software is much more reliable in terms of data
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analysis than the other data analysis tools which were identified in the previous part of this chapter. Creation of the output file from the data is one of the advantages related to the use of this tool as well (Paulus and Bennett, 2017). Frequency distribution of any type of data set can be maintained using this statistical tool as well. This tool is also very useful to draw graphs directly from the SPSS data. All the statistical transformations of the data can be maintained using SPSS (Lewis, 2016). There are other advantages related to this statistical tool as well, such as the control over the data, and a wide range of graphs and charts can be generated using this statistical tool.
On the other hand, there are a few limitations associated with SPSS as well, such as the quality of the graphics which are automatically generated from this software. Although these graphics can be used in academic assignments, they cannot be used in any corporate commercial establishments (Samuels, 2019). This software is also very expensive as compared with all the other statistical tools which are used for analysing raw data. Documentation of the algorithm is very complex and very challenging to decide on.
Descriptive statistics are an integral part of the data analysis method, and were performed using SPSS (Wang and Zhang, 2019). Summarizing the given data set is one of the prime functionalities of descriptive statistics, and the measures of the central tendency and variability are an integral part of descriptive statistics (Green and Salkind, 2016). There are diverse categories of descriptive statistics as well, such as the measure of the frequency of the data, the analysing of the central tendency of the data, measures of data variation, measures of data positioning, etc.
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Primary data for this thesis were collected from participants from different cultural backgrounds (Bhatti et al., 2019). A detailed survey questionnaire was created by the researcher prior to the start of the data collection procedure (Hall, Hume and Tazzyman, 2016). All the participants of this online survey work in public services across the United Arab Emirates. A simple random sampling method was considered as the sampling method to collect the primary raw data. The sample size of the population is 124, and each one of is the sample was made aware of the topic and the purpose of this research.